Open AccessAugmented radial basis function neural network predistorter for linearisation of wideband power amplifiers
Author(s) -
Hui Ming,
Liu Taijun,
Zhang Meng,
Ye Yan,
Shen Dongya,
Ying Xiangyue
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2014.0667
Subject(s) - amplifier , wideband , artificial neural network , control theory (sociology) , nonlinear system , power (physics) , radial basis function , polynomial , computer science , electronic engineering , mean squared error , basis function , mathematics , engineering , telecommunications , bandwidth (computing) , physics , artificial intelligence , control (management) , quantum mechanics , mathematical analysis , statistics
An augmented radial basis function neural network (ARBFNN) is proposed for modelling and linearising a wideband Doherty power amplifier (DPA) with strong memory effects and static nonlinearity. To evaluate the performance of the ARBFNN, a 51 dBm DPA and a 25 MHz mixed test signal were used in modelling and linearisation measurement. Compared with the memory polynomial (MP) model and the real‐valued time‐delay neural network (RVTDNN), the ARBFNN is highly effective, leading to 3 and 5 dB improvements in the normalised mean square error. More importantly, the ARBFNN predistorter represents a significant improvement over the RVTDNN and MP in the suppression of the out‐of‐band spectral regrowth. In addition, the ARBFNN has a similar linearisation capability as the generalised MP model, but has much better numerical stability.